These APIs offer the possibility to work on the structure of the LMF base by adding, modifying or deleting components of LMF model.

there is no interface which facilitates the use of these APIs.


Rozovskaya and Roth (2010b) show that by introducing into native training data artificial article errors it is possible to improve the performance of the article correction system, when compared to a classifier trained on native data.


For every preposition p i , we train a classifier using only examples that are in L1ConfSet(p i ).

to NegAll, for each source preposition, the negative examples are not all other nine types, but only those that belong in L1ConfSet(p i ).


This setting is realistic since most words in each sentence were already classified (correctly).

when moving to active learning, the situation changes.


(i) the greatest improvement has been obtained when the TL uses a poor feature-set; and (ii) when the TL baseline model is rich in resources, we still obtain 0.45 points absolute improvement when using n − P ars.

features extracted from automatically-aligned data, in comparison with the ones extracted from the hand aligned data, have helped the MD model to correct many of the TL baseline model false negatives.


In machine learning-based systems, adapting to a new domain has traditionally involved acquiring additional labeled data and learning a new model from scratch.

recent work has proposed more sophisticated approaches that learn a domain-independent base model, which can later be adapted to specific domains (Florian et Figure 1: Example Customization Requirement Blitzer et al., 2006;Jiang and Zhai, 2006;Arnold et al., 2008;Wu et al., 2009).


C DD and C DSD identify additional instances for an existing type and therefore mainly rely on FD and CD rules.

the customizations that modify existing instances (C EB ,C AT A ,C G ) require CR and CO rules.


We demonstrated that a complex NER annotator built using NERL can be effectively customized for different domains, achieving extraction quality superior to the state-of-the-art numbers.

our experience also indicates that the process of designing the rules themselves is still manual and timeconsuming.


A simple way to address this is a pipeline: first predict entity types, and then condition on these when predicting relations.

this neglects the fact that relations could as well be used to help entity type prediction.


This allows them to work with exact optimization techniques such as (Integer) Linear Programs and still remain efficient.

2 when working on a sentence level they fail to exploit the redundancy present in a corpus.


When we compare to an isolated baseline that makes no use of entity types, our joint model improves average precision by 4%.

it does not outperform a pipelined system.


We also heuristically align our knowledge base to text by making the distant supervision assumption (Bunescu and Mooney, 2007;Mintz et al., 2009).

in contrast to these previous approaches, and other related distant supervision methods (Craven and Kumlien, 1999;Weld et al., 2009;Hoffmann et al., 2010), we perform relation extraction collectively with entity type prediction.


They also perform cross-document probabilistic inference based on Markov Networks.

they do not infer the types of entities and work in an open IE setting.


The joint model fails to correctly predict some entity types that the pipeline gets right, but these tend to appear in contexts where relation instances are easy to extract without considering en-   Manually evaluated precision for New York Times data can be seen in table 2.

to the Wiki setting, here modelling entity types and relations jointly makes a substantial difference.


(2007) use a context sensitive kernel in conjunction with features they used in their earlier publication (GuoDong et al., 2005).

we take an approach similar to Nguyen et al.


Specifically, the "meeting" event, which is an ACE CONTACT event and an INR event according to our definition, is the major cause of overlap.

our type INR has a broader definition than ACE type CONTACT.


PT is a relaxed version of the SST; SST measures the similarity between two PSTs by counting all subtrees common to the two PSTs.

there is one constraint: all daughter nodes of a node must be included.


This regularity in the syntactic predicate-argument structure allows us to overcome lexical sparseness.

in order to exploit such regularities, we need to have access to a representation which makes the predicateargument structure clear.


When using dependency structures, the SST kernel is far less appealing, since it forces us to always consider all daughter nodes of a node.

as we have seen, it is certain daughter nodes, such as the presence of a to PP and a about PP, which are important, while other daughters, such as temporal or locative adjuncts, should be disregarded.


First, we aim to show the importance of using data sampling when evaluating on F-measure; specifically, we expect under-sampling to outperform no sampling, over-sampling to outperform under-sampling, and over-sampling with transformations to out perform over-sampling without transformations.

the social event classification task does not suffer from data skewness because the INR and COG relations; both occur almost the same number of times.


Hence the effects of training and testing a machine learning algorithm for sentiment analysis on data from different domains have been analyzed in previous research.

to the best of our knowledge, these effects have not been investigated regarding the extraction of opinion targets.


We investigated whether the CRF algorithm was overfitting to the training datasets by reducing their size to the size of the cameras dataset.

the reduction of the training data sizes never improved the extraction results regarding F-Measure for the movies, web-serviecs and cars datasets.


Most prior work on the U.S. Congressional Floor Debates dataset focused on using relationships between speakers such as agreement (Thomas et al., 2006;Bansal et al., 2008), and used a global mincut inference procedure.

they require all test instances to be known in advance (i.e., their formulations are transductive).


Our approach can, in principle, be applied to any classification task that is well modeled by jointly solving an extraction subtask.

as evidenced by our experiments, proper training does require a reasonable initial guess of the extracted ex-planations, as well as ways to mitigate the risk of the extraction subtask suppressing too much information (such as via feature smoothing).


Many statistical machine translation systems such as IBM models (Brown et al., 1993) learn word translation probabilities from millions of parallel sentences which are mutual translations.

large scale parallel corpora rarely exist for most language pairs.


In order to classify documents in the target language, a straightforward approach to transferring the classification model learned from the labeled source language training data is to translate each feature from the bag-of-words model according to the bilingual lexicon.

because of the translation ambiguity of each word, a model in the source language could be potentially translated into many different models in the target language.


Note that if the average number of translations for a word w is n and v is the number of words in the vocabulary there are n v possible models m ′ t translated from m s .

we can do the following mathematical transformation on the equation which leads to a polynomial time complexity algorithm.


"UNIGRAM" shows significant improvement over "EQUAL" as the occurrence count of the translation words in the target language can help disambiguate the translations.

occurrence count in a monolingual corpus may not always be the true translation probability.


For instance, the English word "work" can be translated into "工 作(labor)" and "工厂(factory)" in Chinese.

in our Chinese monolingual news corpus, the count for "工厂(factory)" is more than that of "工 作(labor)" even though "工作(labor)" should be a more likely translation for "work".


Both of the verbs can be followed by a clause.

the SRL system regards "is", the predicate of the clause, as the patient, resulting in features like "doubt_A1_is" and "suspect_A1_is", which capture nothing about verb usage context.


For example, (Adar et al., 2007) did a comprehensive corre-lation study among queries, blogs, news and TV results.

different from the content-free analysis above, our work compares the sources based on the content.


For these unfamiliar topics, users possibly search the web "after" they read the news articles and express their diverse opinions in the blog.

on the topics like "insurance rate" or "consolidation loans," Blog is similar to the queries while N ews is not.


News or blog articles consist of completed sentences and paragraphs which would contain plenty of meaningful bigrams.

search queries consist of keywords -relatively discrete and regardless of order.


HOLMES's distinction is that it is domain independent and that its inference time is linear in the size of its input corpus, which enables it to scale to the Web.

hOLMES's Achilles heel is that it requires hand-coded, first-order, horn clauses as input.


Learning Horn clauses has been studied extensively in the Inductive Logic Programming (ILP) literature (Quinlan, 1990;Muggleton, 1995).

learning Horn clauses from opendomain theories is particularly challenging for several reasons.


Prior work in relation discovery (Shinyama and Sekine, 2006) has investigated the problem of finding relationships between different classes.

the goal of this work is to learn rules on top of the discovered typed relations.


For extraction terms with multiple senses (e.g., New York), we split their weight based on how frequently they appear with each class in the Hearst patterns.

many discovered relations are rare and meaningless, arising from either an extraction error or word-sense ambiguity.


On an arbitrary day, the probability of having a storm is fairly low (p(S) 1).

if we know that the atmospheric pressure on that day is low, this substantially increases the probability of having a storm (although that absolute probability may still be small).


The more frequently a fact is extracted from the Web, the more likely it is to be true.

facts in E should have a confidence bounded by a threshold p max < 1.


Setting the weights involves counting the number of true groundings for each rule in the data (Richardson and Domingos, 2006).

the noisy nature of Web extractions will make this an overestimate.


weights, and performing the inference took 50 minutes on a 72 core cluster.

we note that for half of the relations SHERLOCK accepts no inference rules, and remind the reader that the performance on any particular relation may be substantially different, and depends on the facts observed in the corpus and on the rules learned.


TAREC overcomes these limitations by searching and selecting the top relevant articles in Wikipedia for each input term; taxonomic relations are then recognized based on the features extracted from these articles.

information extraction bootstrapping algorithms, such as (Pantel and Pennacchiotti, 2006;Kozareva et al., 2008), automatically harvest related terms on large corpora by starting with a few seeds of pre-specified relations (e.g.


A variety of NLP tasks, including inference, textual entailment (Glickman et al., 2005;Szpektor et al., 2008), and question answering (Moldovan et al., 1999), rely on semantic knowledge derived from term taxonomies and thesauri such as Word-Net.

the coverage of WordNet is still limited in many regions (even well-studied ones such as the concepts and instances below Animals and People), as noted by researchers such as  and (Hovy et al., 2009) who perform automated semantic class learning.


The most productive ones are: "X are Y that" and "X including Y".

the highest yield is obtained when we combine evidence from all patterns.


During a long period of time, researches on question answering are mainly focused on finding short and concise answers from plain text for factoid questions driven by annual trackes such as CLEF, TREC and NTCIR.

people usually ask more complex questions in real world which cannot be handled by these QA systems tailored to factoid questions.


Since the code is not publicly available, we followed the same strategy in the original paper and share 0.1K topics across ideologies and then divide the rest of the topics between ideologies 4 .

unlike our model, there are no internal relationships between these two sets of topics.


Spoken dialect ID relies on speech recognition techniques which may not cope well with dialectal diversity.

the acoustic signal is also available as input.


Again, we compute the pointwise product of all word maps.

to 5.1, we performed some smoothing in order to prevent erroneous word derivations from completely zeroing out the probabilities.


This approach is fairly common for language ID and has also been successfully applied to dialect ID (Biadsy et al., 2009).

it requires a certain amount of training data that may not be available for specific dialects, and it is uncertain how it performs with very similar dialects.


The n-gram system presented above has no geographic knowledge whatsoever; it just consists of six distinct language models that could be located anywhere.

our model yields probability maps of German-speaking Switzerland.


Another reason could be that Wikipedia articles use a proportionally larger amount of proper nouns and low-frequency words which can-   not be found in the lexicon and which therefore reduce the localization potential of a sentence.

one should note that the word-based dialect ID model is not limited on the six dialect regions used for evaluation here.


In the evaluation presented above, the task consisted of identifying the dialect of single sentences.

one often has access to longer text segments, which makes our evaluation setup harder than necessary.


Erk and Padó (2010) propose an exemplar-based model for capturing word meaning in context.

to the prototype-based approach, no clustering takes place, it is assumed that there are as many senses as there are instances.


One potential explanation for the superior performance of the tiered model vs. the DPMM multiprototype model is simply that it allocates more clusters to represent each word (Reisinger and Mooney, 2010).

we find that decreasing the hyperparameter β (decreasing vocabulary smoothing and hence increasing the effective number of clusters) beyond β 0.1 actually hurts multiprototype performance.


Whether we synthesize an AN for generation or decoding purposes, we would want the synthetic AN to look as much as possible like a real AN in its natural usage contexts, and cooccurrence vectors of observed ANs are a summary of their usage in actual linguistic contexts.

it might be the case that the specific resources we used for our vector construction procedure are not appropriate, so that the specific observed AN vectors we extract are not reliable (e.g., they are so sparse in the original space as to be uninformative, or they are strictly tied to the domains of the input corpora).


One of the major transformations used in Linguistic Steganography is synonym substitution.

few existing studies have studied the practical application of this approach.


For example, the words in the WordNet synset {bridge, span} share the meaning of "a structure that allows people or vehicles to cross an obstacle such as a river or canal or railway etc.".

bridge and span cannot be substutited for each other in the sentence "sus- pension bridges are typically ranked by the length of their main span", and doing so would likely raise the suspicion of an observer due to the resulting anomaly in the text.


The disadvantage of Bolshakov's system is that all words in a synonym transitive closure chain need to be considered, which can lead to very large sets of synonyms, and many which are not synonymous with the original target word.

our proposed method operates on the original synonym sets without extending them unnecessarily.


In particular the constituent labels are highly ambiguous, firstly we don't know a priori how many there are, and secondly labels that appear high in a tree (e.g., an S category for a clause) rely on the correct inference of all the latent labels below them.

recent work on the induction of dependency grammars has proved more fruitful (Klein and Manning, 2004).


The IBM models look for an equivalence relationship between lexical items in two languages, whereas DMV addresses functional relationships between two elements with distinct meanings.

both attempt to model a similar set of factors, which they posit will be important to their respective tasks 1 .


On the latter dataset, even Model 1 outperforms the right-branching baseline.

the Danish dataset is unusual (see Buchholz and Marsi 2006) in that the alternate adjacency baseline of leftbranching (also mentioned by Klein and Manning 2004) is extremely strong and achieves 48.8% directed accuracy.


These approaches used language-specific templates to propose new lexical items and also required as input a set of hand-engineered lexical entries to model phenomena such as quantification and determiners.

the use of higher-order unification allows UBL to achieve comparable performance while automatically inducing these types of entries.


As with their work, we also use nonparametric priors for category refinement and em-ploy variational methods for inference.

our goal is to apply category refinement to dependency parsing, rather than to PCFGs, requiring a substantially different model formulation.


For instance, in the following discussion Alice's sentence has her opinion against something, yet no attitude toward the recipient of the sentence, Bob.

alice: "You know what, he turned out to be a great disappointment" Bob: "You are completely unqualified to judge this great person" Bob shows strong attitude toward alice.


The fragments we extracted earlier are more relevant to our task and are more suitable for further analysis.

these fragments are completely lexicalized and consequently the performance of any analysis based on them will be limited by data sparsity.


For example, in some applications we might be interested in very high precision even if we lose recall, while in other applications we might sacrifice precision in order to get high recall.

we notice that the baselines always have low precision regardless of recall.


Clearly, the source language (English in our experiments) benefits from being paired with any target language.

some languages seem to give substantially better results than others when used as the conjugate language.


Previous work on this problem has relied on either counting methods or lexico-syntactic patterns.

determining whether a relation is functional, by analyzing mentions of the relation in a corpus, is challenging due to ambiguity, synonymy, anaphora, and other linguistic phenomena.


Overall, CLEANLISTS finds the very high precision points, because of its use of clean data.

it is unable to make 23.1% of the predictions, primarily because the intersection between the corpus and Freebase entities results in very little data for those relations.


<Harry Potter 5, was published in, 2003>.

'was published in(Language)' is not functional, e.g.


We deterministically compute multinomial parameters β by exponentiating and normalizing: This normalization could introduce identifiability problems, as there are multiple settings for η that maximize P (w|η) (Blei and Lafferty, 2006a).

this difficulty is obviated by the priors: given µ and σ 2 , there is only a single η that maximizes P (w|η)P (η|µ, σ 2 ); similarly, only a single µ maximizes P (η|µ)P (µ|a, b 2 ).


Within this paradigm, computational techniques are often applied to post hoc analysis: logistic regression (Sankoff et al., 2005) and mixed-effects models (Johnson, 2009) are used to measure the contribution of individual variables, while hierarchical clustering and multidimensional scaling enable aggregated inference across multiple variables (Nerbonne, 2009).

in all such work it is assumed that the relevant linguistic variables have already been identified-a timeconsuming process involving considerable linguistic expertise.


In the general case, finding y * = argmax y∈Y f (y) under this definition of f (y) is an NP-hard problem.

for certain definitions of f i , it is possible to efficiently compute argmax y |i ∈Z i f i (y |i ) for any value of i, typically using dynamic programming.


Joint models have been proposed to overcome this problem (Poon and Vanderwende, 2010;.

besides not being as accurate as their pipelined competitors, mostly because they do not yet exploit the rich set of features used by Miwa et al.


An advantage of ILP inference are guaranteed certificates of optimality.

in practice we also gain certificates of optimality for a large fraction of the instances we process.


It can be verified that this derivation is a valid member of Y .

y(i) = 1 for several values of i: for example, words 1 and 2 are translated 0 times, while word 3 is translated twice.


This means that a mixture of transition matrices will not necessarily yield a meaningful transition matrix.

for dependency grammar, there are certain universal dependencies which appear in all helper languages, and therefore, a mixture between multinomials for these dependencies still yields a useful multinomial.


The previous section focused on transferring an English parser to a new target language.

there are over 20 treebanks available for a variety of language groups including Indo-European, Altaic (including Japanese), Semitic, and Sino-Tibetan.


(2010) also used bilingual treebanks and made use of tree structures on the target side.

the bilingual treebanks are hard to obtain, partly because of the high cost of human translation.


The above case uses the human translation on the target side.

there are few humanannotated bilingual treebanks and the existing bilingual treebanks are usually small.


Our method performed better in terms of grammaticality, equally well in meaning preservation, and worse in diversity, but it could be tuned to obtain higher diversity at the cost of lower grammaticality, whereas it is unclear how the system we compare against could be tuned this way.

an advantage of the paraphraser we compared against is that it always produces paraphrases; by contast, our system does not produce paraphrases when no paraphrasing rule applies to the source sentence.


Towards automatically deriving an aspect hierarchy from the reviews, we could refer to traditional hierarchy generation methods in ontology learning, which first identify concepts from the text, then determine the parent-child relations between these concepts using either pattern-based or clusteringbased methods (Murthy et al., 2010).

pattern-based methods usually suffer from inconsistency of parent-child relationships among the concepts, while clustering-based methods often result in low accuracy.


Thus, by directly utilizing these methods to generate an aspect hierarchy from consumer reviews, the resulting hierarchy is usually inaccurate, leading to unsatisfactory review organization.

domain knowledge of products is now available on the Web.


Such domain knowledge is useful to help organize the product as- pects into a hierarchy.

the initial hierarchy obtained from domain knowledge usually cannot fit the review data well.


For implicit aspect identification, some works proposed to define rules for identification (Liu el al., 2005), while others suggested to automatically generate rules via mutual clustering (Su et al., 2008).

there are some related works on sentiment classification (Pang and Lee, 2008).


Pollack's recursive auto-associative memories (RAAMs) are similar to ours in that they are a connectionst, feedforward model.

rAAMs learn vector representations only for fixed recursive data structures, whereas our rAE builds this recursive data structure.


Most previous work is centered around a given sentiment lexicon or building one via heuristics (Kim and Hovy, 2007;Esuli and Sebastiani, 2007), manual annotation (Das and Chen, 2001) or machine learning techniques (Turney, 2002).

we do not require an initial or constructed sentiment lexicon of positive and negative words.


(2007) extended Marcu's work by using parameter opitimization, topic segmentation and syntactic parsing.

syntactic parsers were usually costly and impractical when dealing with large scale of text.


The drop of precision may be caused by the neglect of structural and semantic information of discourse instances.

m&E still outperformed Baseline in average F -score.


When modeling only positive and negative labels for sentiment, negators are generally treated as flipping the polarity of the adjective it modifies (Choi and Cardie, 2008;Nakagawa et al., 2010).

recent work (Taboada et al., 2011;Liu and Seneff, 2009) suggests that the effect of the negator when ordinal sentiment scores are employed is more akin to dampening the adjective's polarity rather than flipping it.


Each of the above, however, uses a binary rather than an ordinal sentiment scale.

our proposed method for phraselevel sentiment analysis is inspired by recent work on distributional approaches to compositionality.


In our work we propose a single unified model for handling all words of any part of speech.

there has been some research in trying to model compositional effects for sentiment at the phrase-and sentence-level.


As Table 3 shows, the WSJ training set contains only 0.7% imperative sentences.

5 our test sentences from the web contain approximately 10% imperatives.


Since it explicitly models the grammaticality of the output via target-side syntax, the string-to-tree model (Xiao et al., 2010) significantly outperforms both the state-of-the-art phrase-based system Moses (Koehn et al., 2007) and the formal syntax-based system Hiero (Chiang, 2007).

there is a major limitation in the string-to-tree model: it does not utilize any useful source-side syntactic information, and thus to some extent lacks the ability to distinguish good translation rules from bad ones.


Given a test source sentence with its parse tree, we can according to this strategy choose only the rules whose source syntax matches the test source tree.

this restriction will rule out many potentially correct rules.


We adopt the latter approach for efficient decoding with integrated ngram language models since this binarization technique has been well studied in string-to-tree translation.

when the rules' source string is decorated with syntax (fuzzy-tree to exact-tree rules), how should we binarize these rules?


This model significantly outperforms the state-of-the-art hierarchical phrasebased model (Chiang, 2005).

those stringto-tree systems run slowly in cubic time (Huang et al., 2006).


In the second example, the Chinese input holds a long distance dependency "中 国 赞 赏 ... 努 力" which corresponds to a simple pattern "noun phrase+verb+noun phrase".

due to the modifiers of "努 力" which contains two subsentences including 24 words, the sentence looks rather complicated.


5 A general weakness of subgradient algorithms is that they do not have this capacity, and so are usually stopped by specifying a maximum number of iterations.

aDMM allows to keep track of primal and dual residuals (Boyd et al., 2011).


Hence, our method seems to be more suitable for decompositions which involve "simple slaves," even if their number is large.

this does not rule out the possibility of using this method otherwise.


Note that a subgradient-based method could handle some of those parts efficiently (arcs, consecutive siblings, grandparents, and head bigrams) by composing arc-factored models, head automata, and a sequence labeler.

no lightweight decomposition seems possible for incorporating parts for all siblings, directed paths, and non-projective arcs.


Streaming approaches (Muthukrishnan, 2005) provide memory and time-efficient framework to deal with terabytes of data.

these approaches are proposed to solve a singe problem.


Metrics like LLR would assign high association scores to frequent words, also leading to incorrect parses.

with a small amount of linguistic side information (Druck et al., 2009;Naseem et al., 2010), we see that these issues can be overcome.


Yielding an accuracy of 76% and F-score of .73 its performance is lower than that of the supervised SVM.

it does outperform both CRFbased methods.


For instance, in the example sentence provided at the beginning of this section, the words "arrested" and "killed" probably have a relatively high apriori likelihood of being ca-sually related.

knowing that the connective "because" evokes a contingency discourse relation between the text spans "The police arrested him" and "he killed someone" provides further evidence towards predicting causality.


For a verb (verbal predicate), we extract its subject and object from its associated dependency parse.

since events are also frequently triggered by nominal predicates, it is important to identify an appropriate list of event triggering nouns.


Two annotators annotated the documents for causal event pairs, using two simple notions for causality: the Cause event should temporally precede the Effect event, and the Effect event occurs because the Cause event occurs.

sometimes it is debatable whether two events are involved in a causal relation, or whether they are simply involved in an uninteresting temporal relation.


's (2010) approach, which combines an incremental parser with a vector-space model of semantics.

this approach only provides a loose integration of the two components (through simple addition of their probabilities), and the notion of semantics used is restricted to lexical meaning approximated by word co-occurrences.


There remains a possibility that the model primarily captures lexical priming, rather than co-reference.

we note that string match is a strong indicator of two NPs being corefer-ent (cf.


In the current implementation of the model, λ is constant throughout the test runs.

λ could possibly be a function of the previous discourse, allowing for more complicated classification probabilities.


However, the model lacked generality, being designed to deal with only one type of sentence.

to both of these earlier models, the model proposed here aims to be general enough to provide estimated reading times for unrestricted text.


Their model has similarities with our own in that it learns correspondences between the alphabets of pairs of languages.

their correspondences are probabilistic and implicit while ours are hard and explicit.


As indicated in the table, the raw number of nominal word types varies quite a bit across the languages, almost doubling from 4,178 (English) to 8,051 (Hungarian).

the number of stems appearing within these words is relatively stable across languages, ranging from a minimum of 3,112 (Bulgarian) to a maximum of 3,746 (Hungarian), an increase of just 20%.


In fact, with only one training language, our method performs worse (on average) than the Linguistica baseline.

with two or more training languages available, our method achieves superior results.


In practice, they are usually trained by maximising the conditional log-likelihood (CLL) of the training data.

it is widely appreciated that optimizing for task-specific metrics often leads to better performance on those tasks (Goodman, 1996;Och, 2003).


The results confirm that the loss-trained models improve over a likelihood-trained baseline, and furthermore that the exact loss functions seem to have the best performance.

the approximations are extremely competitive with their exact counterparts.


For instance, morphological innovations and irregular sound changes can completely obscure relationships between words in different languages.

in the case of reconstruction, an unexplainable word is simply that: one can still correctly reconstruct its ancestor using words from related languages.


Instead, it can at best only select group A or group B as the value for the parent, and leave the other group fragmented as two innovations, as in Figure 3c.

hK10 can recover this relationship (Figure 3d), but this power is precisely what makes it intractable.


For instance, consider the words for "meat/flesh" in the Formosan languages: Squliq /hiP/, Bunun /titiP/, Paiwan /seti/, Kavalan /PisiP/, CentralAmi /titi/, Our system groups all of these words except for Squliq /hiP/.

despite these words' similarity, there are actually three cognate groups here.


The table also shows that linearly interpolating the translation models improved the overall BLEU score, as expected.

using multiple decoding paths, and no explicit model merging at all, produced even better results, by 2 BLEU points over the best individual model and 1.3 BLEU over the best interpolated model, which used λ = 0.9.


We presented in Section 5 several methods to improve the performance of a single general-domain translation system by restricting its training corpus on an information-theoretic basis to a very small number of sentences.

section 6.3 shows that using two translation models over all the available data (one in-domain, one general-domain) outperforms any single individual translation model so far, albeit only slightly.


This is a large multilingual corpus, containing sentences translated from several European languages.

it is organized as a collection of bilingual corpora rather than as a single multilingual one, and it is hard to identify sentences that are translated to several languages.


Furthermore, we show that translated LMs are better predictors of translated sentences even when the LMs are compiled from texts translated from languages other than the source language.

lMs based on texts translated from the source language still outperform lMs translated from other languages.


The simplest variation is TESLA-M 2 , based on matching bags of ngrams (BNG) like BLEU.

unlike BLEU, TESLA-M formulates the matching process as a real-valued linear programming problem, thereby allowing the use of weights.


Similarly, TESLA-F and TESLA-M gave different outputs for only 857 sentences, or 34%.

bLEU and TESLA-M gave different translations for 2248 sentences, or 90%.


Interestingly, the human translations average only 22 words, so BLEU and TER translations are in fact much closer on average to the reference lengths, yet their translations often feel too short.

manual inspections reveal no tendency for TESLA-F and TESLA-M to produce overly long translations.


Its abstract goal, on the one hand, could be pre-processing of the linguistic signal, to enable subsequent stages of analysis.

it could be making explicit the (complete) contribution that the grammatical form of the linguistic signal makes to interpretation, working out who did what to whom.


Our barerel category corresponds to their "object reduced relative" category with the difference that we also include adverb relatives, where the head noun functions as a modifier within the relative clause, as does time in (1).

our rnr category is somewhat narrower than Rimell et al.


In Steps (a-c), for the current example i, we compute the relative loss function ∆ i that scales with the loss achieved by the best and worst possible parses under the model.

to previous work, we do not only compute the loss over a fixed n-best list of possible outputs, but instead use the current model score to recompute the options at each update.


Faced with thousands of news documents, people usually have a myriad of interest aspects about the beginning, the development or the latest situation.

traditional information retrieval techniques can only rank webpages according to their understanding of relevance, which is obviously insufficient (Jin et al., 2010).


Probable bias is enlarged by searching for worthy sentence in single dates.

precision drops due to ex-cessive choice of global timeline-worthy sentences.


In addition sentences generated by T * are also succinct: with an average length of 6.9 words per sentence.

we are still some way off the human gold standard since we do not predict other parts-of-speech such as adjectives and adverbs.


In order to address this issue, they computed the translation-bytranslation correlation with human assessments (i.e., correlation at the sentence level).

correlation with human judgements is not enough to determine the reliability of measures.


Additive Reliability According to the previous properties, corroborating evaluation results with several measures increases the reliability of evaluation results at the cost of sensitivity.

increasing the score threshold of a single measure should have a similar effect.


Still, the pairs {(X k , Y k )} n k=1 , and therefore the differences {Z k } n k=1 , are iid which is what makes paired testing valid.

there is no theoretical distribution for T from which to calculate valid quantiles c for cutoffs, and therefore the use of the unpaired t-statistic cannot be recommended for TAC evaluation.


5.4 leading up to Table 5.4.7 on p. 167) that the Wilcoxon signed-rank statistic W provides greater robustness and often much greater efficiency than the paired T, with ARE which is 0.95 with f a standard normal density, and which is never less than 0.864 for any symmmetric density f .

in our context, continuous scores such as pyramid exhibit document-specific score differences between summarizers which often have approximately normal-looking histograms, and although the alternatives perhaps cannot be viewed as pure location shifts, it is unsurprising in view of the ARE theory cited above that the W and T paired tests have very similar performance.


In contrast to the human vs. machine case, we do not know the truth here.

since the number of significant differences increases with paired testing here as well, we believe this also reflects the greater discriminatory power of paired testing.


Typically, these sorts of features are probabilities estimated from a corpus parsed using a supervised parser.

there do not currently exist treebanks with annotated phrase  Table 3: Most probable child phrases for the parent phrase "made up" for each direction, sorted by the conditional probability of the child phrase given the parent phrase and direction.


If the score of the partial structure can only get worse when combining it with other structures (e.g., in a PCFG), then the first time that we pop an item of type GOAL from the agenda, we are guaranteed to have the best parse.

in our model, some features are positive and others negative, making this property no longer hold; as a result, GOAL items may be popped out of order from the agenda.


Competitive performance is also found by using the unsupervised Chinese parser and supervised English parser (+0.53 over Moses).

when using unsupervised parsers for both languages, performance was below that of Moses.


The system we used is still in an experimental state and probably not quite at the state-of-the-art level yet.

we considered it was good enough for our purpose, since we mainly want to test our algorithm is a practical way.


Therefore, it is not reasonable in general to try to compute all these treelets.

we are not really interested in computing all possible treelets.


(2011) show that super-vised methods recognize such relations with high accuracy.

large sets of annotated relations need to be provided for this purpose.


Therefore, the PRA approach achieves high recall partially by combining a large number of unspecialized paths, which correspond to unspecialized rules.

learning more accurate specialized paths is part of our future work.


The underlying assumption is that all words in a document constitute the context of the target word.

it is not the case in real world corpora.


A word is assumed to be generated by first sampling a topic, then choosing a path from the root node of hierarchy to a sense node corresponding to that word.


We restricted our sample to tweets from accounts which indicated their primary language as English.

there may be some foreign language messages in our dataset, since multi-lingual users may tweet in other languages even though their account is marked as "English".


Previous work has shown that such analysis can be more difficult than topic-based analysis (Pang and Lee, 2008), and we have the additional challenge that comments are typically much shorter than full-length articles.

the difficulty in analyzing the textual information in comments can be alleviated by additional contextual information such as author identities.


Better approaches to estimate similarities have also been proposed in Koren (2010).

modern methods based on matrix factorization have been shown to outperform nearest neighbor methods (Salakhutdinov and Mnih, 2008a,b;Bell et al., 2007).


Metrics p-value vv+uc > vv All < 10 −7 vv+uc > uc All < 10 −20 uc > bilinear All except P@5 < 0.006 bilinear > svm All < 10 −20 vv > svm All < 10 −20 svm > nb All < 10 −8 nb > cos All < 10 −20 Not surprisingly, vv performs poorly for raters or authors with no ratings observed in the training data.

once we have a small amount of ratings, it starts to outperform uc, even though intuitively, the textual information in the comment should be more informative than the authorship information alone.


Response generation should also be beneficial in building "chatterbots" (Weizenbaum, 1966) for entertainment purposes or companionship (Wilks, 2006).

we are most excited by the future potential of data-driven response generation when used inside larger dialogue systems, where direct consideration of the user's utterance could be combined with dialogue state (Wong and Mooney, 2007;Langner et al., 2010) to generate locally coherent, purposeful dialogue.


We had expected human annotators to pick up on these fluency errors, giving the the advantage to the IR systems.

it appears that MT-CHAT's ability to tailor its response to the status on a fine-grained scale overcame the disadvantage of occasionally introducing fluency errors.


Consider predicting the number of downloads over g future time steps.

if t is the time of forecasting, we can observe the texts of all articles published before t. any article published in the interval [t − g, t] is too recent for the outcome measurement of y to be taken.


According to our segmentation criteria, it consists of two words " " (tsuneyama) and " " (jou).

the morphological analyzer wrongly segments it into " " (tsune) and " " (yamashiro) because " " (tsuneyama) is an unknown word.


Type-based sampling (Liang et al., 2010) has the ability to directly escape a local optimum, making inference very efficient.

type-based sampling is not easily applicable to the bigram model owing to sparsity and its dependence on latent assignments.


One may instead consider a pipeline approach in which we first extract noun phrases in text and then identify boundaries within these noun phrases.

noun phrases in text are not trivially identifiable in the case that they contain unknown words as their constituents.


Up to this point, we consider every possible boundary position.

this seems wasteful, given that a large portion of text has only marginal influence on the segmentation of the noun phrase in question.


For G to be a uniform distribution over an infinite lexeme set L, we need L to be uncountable.

9 it turns out 10 that with probability 1, each L t is countably infinite, and all the L t are disjoint.


rare forms (Bin 1), which are mostly regular, and worst on on the 10 most frequent forms of the language (Bin 5).

adding a corpus helps most in fixing the errors in bins with more frequent and hence more irregular verbs: in Bins 2-5 we observe improvements of up to almost 8% absolute percentage points.


In this model, inference on sequences is modeled as cascaded decision.

the decision on a sequence labeling sequel to other decisions utilizes the features on the preceding results as marginalized by the probabilistic models on them.


Such a framework can maximize reusability of existing sequence labeling systems.

it exhibits a strong tendency to propagate errors to upper labelers.


It enables simultaneous learning and estimation of multiple sequence labelings on the same input sequences, where time slices of the outputs of all the out sequences are regularly aligned.

it puts the distribution of states into Bayesian networks with cyclic dependencies, and exact inference is not tractable in such a model in general.


From the definition ofh 1 (3), each element of the third factor of (13) ∂h 1 ∂θ 1 becomes There exists efficient dynamic programming to calculate the covariance value (17) (without goint into that detail because it is very similar to the one shown later in this paper), and of course we can run such dynamic programming for ∀ k ′ 1 ∈ K ′ 1 , e 1 ∈ E 1 .

the size of the Jacobian ∂h 1 ∂θ 1 is equal to Since it is too large in many tasks likely to arise in practice, we should avoid to calculate all the elements of this Jacobian in a straightforward way.


The other baseline method has a CRF model for the chunking labeling, which uses the marginalized features offered by the POS labeler.

the parameters of the POS labeler are fixed in the training of the chunking model.


Note that this model with multiple context features is deficient: it can generate data that are inconsistent with any actual corpus, because there is no mechanism to constrain the left context word of token e i to be the same as the right context word of token e i−1 (and similarly with alignment features).

deficient models have proven useful in other unsupervised NLP tasks (Klein and Manning, 2002;Toutanova and Johnson, 2007).


Distance/similarity feature spaces are more suitable to the paraphrase detection task because they model the similarity between the two texts.

entailment trigger and content feature spaces model complex relations between the texts, taking into account first-order entailment rules, i.e.


For example, consider the following feature: This feature is active for the pair ("GM bought Opel","GM owns Opel"), with the variable unification X = "GM" and Y = "Opel".

this feature is not active for the pair ("GM bought Opel","Opel owns GM") as there is no possibility of unifying the two variables.


In the quali-tative analysis that follows, we show some examples that support this intuition.

syntax plays a key role for detecting redundancy.


Second, their approach involves qualitative analysis of the collected data only a posteriori, after manual removal of invalid and trivial generated hypotheses.

our approach integrates quality control mechanisms at all stages of the data collection/annotation process, thus minimizing the recourse to experts to check the quality of the collected material.


On the one hand, as expected, the more creative "Add Info" task proved to be more demanding than the "Remove Info": even though it was paid more, it still took little more time to be completed.

although the "Unidirectional Entailment" task was expected to be more difficult and thus rewarded more than the "Bidirectional Entailment" one, in the end it took notably less time to be completed.


This avoids the need for gathering training data on every verb or adjective for which we want to determine whether it is being used metaphorically or literally, since the algorithm is not sensitive to the specific target word.

the performance might improve if the target word were included in the feature vectors.


By modifying the situation given to workers, it is likely we can expand our collection to better represent other groups of AAC users, such as those using predictive keyboards or eye-trackers.

obtaining data representative of users with cognitive or language impairments via crowdsourcing would probably be difficult.


Our evaluation of this false positive set showed that its accuracy dropped by 6% compared to the NoDef system.

the StatDef system outperformed the two other systems, and its accuracy improvement upon the RuleDef system is statistically significant at p<0.05.


The results were derived by experimenting with a TREC dataset 11 (Li and Roth, 2002), reaching an accuracy of 91.8%.

such data refers to typical instances from QA, whose syntactic patterns can be easily generalized by STK.


However, such data refers to typical instances from QA, whose syntactic patterns can be easily generalized by STK.

we have shown that STK-CT is not effective for our domain, as it presents very innovative elements: questions in affirmative and highly variable format.


We found that this problem has been largely resolved in the current release.

1,949 tokens and 36 MWE spans still lacked tags.


2 We used the 80/10/10 split described by Crabbé and Candito (2008).

they used a previous release of the treebank with 12,531 trees.


Integrating out the parameters, we have:  (1) and(2) above have the same type since t(z, s 1 ) = t(z, s 2 ).

the two sites conflict since the probabilities of setting b s1 and b s2 both depend on counts for the tree fragment rooted at NP.


It also matches the ordering for English (Cohn et al., 2010;Liang et al., 2010).

the standard baseline for TSG models is a simple parent-annotated PCFG (PA-PCFG).


We chose French for these experiments due to the pervasiveness of MWEs and the availability of an annotated corpus.

mWE lists and syntactic treebanks exist for many of the world's major languages.


For example, /lkn/but signals almost always a CON-TRAST relation.

there are connectives where this is not the case, such as /mnd /since which has a CAUSAL and a TEMPORAL sense.


For example, the arguments of the relation TEMPORAL.SYNCHRONOUS are likely to have the same tense.

arg1 tense is more likely to be prior to arg2 tense for TEMPO-RAL.ASYNCHRONOUS and CAUSE relations.


We designed our definitions and guidelines to reflect language use in the text genre of message board posts, trying to be as domain-independent as possible so that these definitions should also apply to message board texts representing other topics.

we give examples from the veterinary domain to illustrate how these speech act classes are manifested in our data set.


In message boards, these types of Commissives are relatively rare.

we found many statements where the main purpose was to confirm to the readers that the writer would perform some action in the future.


we saw many posts where that inference would have been wrong.


The lexical and syntactic features dramatically improve performance on Commissives, increasing F score from 27% to 40%, and they produce a 2% recall gain for Representatives but with a corresponding loss of precision.

we observed that only a few of the lexical and syntactic features had much impact on performance.


From the first example the CSD could learn even that the bag-of-word contains brother or the SUBJ=brother.

in the second example, the bag-of-word representation is not sufficient to learn that the local context of Newcastle is altered because it is the subset of the bag-of-word representation of Arsenal's non-altered local context.


Indicator selectors can be even derived from most classifiers which are based on feature weighting (like MaxEnt and AvgPerceptron) or feature ranking (like rule-based classifiers) 5 as well.

indicator selection is not the focus of this For our experiments, a feature evaluation-based greedy algorithm was employed to select the set of indicators from the pool of token uni-and bigrams.


The improvement of voting function is statistically significant at a 99.6% confidence level by conducting Wilcoxon Matched-Pairs Signed-Ranks Test on the 10 folds of the testing set.

the improvement of average function is not significant at the 0.05 level which implies that average is inferior to voting.


A comprehensive survey is presented in (Rokach, 2009).

ensemble-based ranking has only recently attracted research interests (Hoi and Jin, 2008;Wei et al., 2010).


With respect to (iii), the above-mentioned studies use ad hoc thresholds to separate compositional and non-compositional phrases but do not offer a principled decision criterion.

2 we train a statistical classifier to learn a decision criterion.


In the latter case, the modifier(s) introduce(s) a non-compositional, unpredictable shift of meaning; hot shifts the meaning of dog from live animal to food.

the compositional meaning shift caused by small in small dog is transparent.


This shows the effectiveness of using the principled query expansion technique coupled with KL-divergence retrieval model to rank KB entries.

again we observe that the effects on the Nil and the non-Nil queries are different.


For entity diambiguation they used the contextual comparisons between the Wikipedia article and the KB article.

their work ignores the possibilities of acronyms in the entities.


Template construction is usually done manually by domain experts, and annotated documents are often created to facilitate supervised learning approaches to IE.

both manual template construction and data annotation are labor-intensive.


Such a complex task calls for a combination of multiple approaches, and much research indeed suggests "hybrid" approaches to MWE identification (Duan et al., 2009;Weller and Fritzinger, 2010;Ramisch et al., 2010;Hazelbeck and Saito, 2010).

we believe that Bayesian Networks provide an optimal architecture for expressing various pieces of knowledge aimed at MwE identification, for the following reasons (Heckerman, 1995): • to many other classification methods, BN can learn (and express) causal relationships between features.


The histogram for this candidate is thus (75, 15, 8, 2).

the non-MWE txwm mepv (domain-of law) "domain of the law", which is syntactically identical, occurs in nine different inflected forms, and its sorted histogram is (59, 14, 7, 7, 5, 2, 2, 2, 2).


Several techniques have been proposed to deal with these ambiguity cases (Tanaka and Umemura, 1994;Shirai and Yamamoto, 2001;Bond et al., 2001;Paik et al., 2004;Kaji et al., 2008;Shezaf and Rappoport, 2010).

each technique has different performance and properties producing dictionaries of certain characteristics, such as different levels of coverage of entries and/or translations.


Sjöbergh (2005) compared full definitions in order to detect words corresponding to the same sense.

not all the dictionaries provide this kind of information.


IC depends on the structure of the source dictionaries.

dS depends on a good comparable corpus and translation process.


This improvement is more marked with the strictest thresholds (T OP 1 , 0.1).

if global thresholds are used, performance starts to decline significantly when dealing with words whose frequency is above 1,000.


As for average precision, IC provides better results than DS if all entries are taken into account.

dS tips the scales in its favor if only entries with frequencies above 50 are considered and strict thresholds are used (T OP 1 , 0.1).


Hierarchical rules whose lexical evidence helps resolve words locally will also be favored by our cohesion penalty feature.

ignorant of the syntactic structure, the glue rule penalty may penalize a reasonably cohesive derivation such as Derivation 5 and at the same time promote a less cohesive hierarchical translation, such as Derivation 6.


The baseline system translates dui (English to) as "of the" and misorders the sentence.

the feature-augmented model "bin-2" cap-  tures the boxed area as a whole and uses Rule 10 to perform the right global reordering.


In fact, it constrains reordering for the phrase-based model, as Cherry finds that the cohesion constraint is used "primarily to prevent distortion" and to provide "an intelligent estimate as to when source order must be respected" (Cherry, 2008).

since the hierarchical phrasebased model already conducts principled reordering search with rules through the more constrained chart-decoding, ill-formed derivations exhibit themselves more often as nonconstituent translation than interrupted translation as defined in (Cherry, 2008;Bach et al., 2009a,b) (They do have a non-empty intersection, but neither subsumes the other).


(2005) proposed a discriminative language modeling approach that uses mixtures of POS and surface information and showed that it leads to a reduction in speech recognition word er-ror rates.

their approach seems more suited for n-best list re-ranking and it is not clear whether those improvements carry over to machine translation.


Motivated by these work, we use a translation forest (Section 3) which contains both "reference" derivations that potentially yield the reference translation and also neighboring "non-reference" derivations that fail to produce the reference translation.

1 the complexity of generating this translation forest is up to O(n 6 ), because we still need biparsing to create the reference derivations.


The motivation of using such a forest is efficiency.

since this space contains both "good" and "bad" translations, it still provides evidences for discriminative training.


this may introduce rules with more than two variables and increase the complexity of bi-parsing.


The window size restrictions mean that some reference alignments are not reachable from the starting point.

this is unlikely to limit performance -an oracle aligner achieves 97.6%F -measure on the Arabic-English training set.


Again the resulting improvement over the ME-seq aligner is statistically significant.

here the improvement in recall is somewhat larger than the improvement in precision.


Given a test document, our approach imitates this procedure by first retrieving similar bilingual document pairs from the training parallel corpus, which has often been applied in IR-based adaptation of SMT systems (Zhao et al.2004;Hildebrand et al.2005;Lu et al.2007) and then extracting bilingual phrase pairs from similar bilingual document pairs to store them in a static cache.

such a cache-based approach may introduce many noisy/unnecessary bilingual phrase pairs in both the static and dynamic caches.


(1992) and gave a detailed comparison and analysis of the "one translation per discourse" hypothesis.

she failed to propose an effective way to integrate document-level information into a SMT system.


Previous cache-based approaches mainly point to cache-based language modeling (Kuhn and Mori, 1990), which uses a large global language model to mix with a small local model estimated from recent history data.

applying such a language model in SMT is very difficult due to the risk of introducing extra noise (Raab, 2007).


We don't think the translation quality for example 4 in our system is worse than Moses.

the translation quality for example 3 in our system is very bad and especially showed on "re-ordering".


In this experiment, we only adopt the flat data in our cache.

the structured data may improve the correctness of matching and thus effectively avoid noise.


We make rather little use of the dual form of the problem.

the complementary slackness conditions that are necessary for optimality to hold play an important role in the next section in which we present a reformulation of the relaxed maximum entropy problem.


Traditional approaches for active learning query the human experts to obtain the labels for intelligently chosen data samples.

in text classification where the input data is generally represented as documentword matrices, human supervision can be obtained on both documents and words.


This demonstrates that splitting a katakana noun compound is not at all a trivial task to resolve, even for the state-of-theart word segmentation systems.

pROpOSED outperformed both JUMAN and MECAB in this task, meaning that our technique can successfully complement the weaknesses of the existing word segmentation systems.


This shows that the back-transliteration feature successfully reduced the number of out-ofvocabulary words.

we observed that the paraphrase and back-transliteration features were activated for 79.5% (1926/2423) and 15.5% (376/2423) of the word boundaries in our test data.


Similarly, we can discretize the punctuation variety features.

we only set one threshold, 30, for this value.


In spite of its simplicity, the document-based features can help the task.

when we combine statistics-based features with document-based features, we cannot get further improvement in terms of F-score.


In this section, we limit our discussion of logical metonymy to the verb-object case, its corresponding baseline for ranking interpretations, and our proposed enhancements.

similar baselines exist for other types of logical metonymy, such as adjective-noun and noun-noun.


Both metonymy data sets were limited to the verbs found in Lapata and Lascarides (2003), which are still quite common (attempt, begin, enjoy, expect, finish, prefer, start, survive, try, want).

the verbs used in our data set had a greater number of WordNet senses attested in a corpus than the SemEval data (an average of 4.4 senses for our data versus 3.0 senses for the SemEval data).


WordNet has been used in earlier studies (Hirst and St-Onge, 1998;Jiang and Conrath, 1997;Lin, 1998;Leacock and Chodorow 1998;Resnik, 1995;Seco et al., 2004;Wu and Palmer, 1994) and is still a preferred knowledge source in recent works (Agirre et al., 2009).

its effectiveness may be hindered by its lack of coverage of specialized lexicons and domain specific concepts (Strube and Ponzetto, 2006;Zhang et al., 2010).


On one hand, WordNet is a lexical resource containing rich and strict semantic relations between words, but lacks coverage of specialized vocabularies.

wikipedia is a semi-structured resource with good coverage of domains and named entities, but the semantic knowledge is organized in a looser way.


For example, the node piano has a density value of 15 under the node percussion instrument.

the density value of its hyponyms Grand piano, upright piano, and mechanical piano, is only 3.


Also note the resemblance to Mitchell and Lapata's best scoring vector composition model which, likewise, uses pointwise multiplication.

the model presented here has two advantages.


The number of clusters (K) and levels (L) were inferred automatically for HGFC as described in section 3.2.3.

to make the results comparable with previously published ones, we cut the resulting hierarchy at the level of closest match (12 clusters) to the K (13) in the gold-standard.


31.2.0 and 31.2.1 which both belong to 31.2 Admire verbs).

the remaining 8 clusters group together sub-classes (and their members) belonging to unrelated parent classes.


On one hand, previous work shows that there is a substantial lack of automatic methods for engineering lexical/syntactic features (or more in general syntactic/semantic similarity).

automatic feature engineering of syntactic or shallow semantic structures has been carried out by means of structural kernels, e.g.


Thus, it just gives an additional weight to the fragment and does not violate the Mercer's conditions.

the multiplication by σ(n 1 , n 2 ) does depend on both comparing examples, i.e.


This is useful to measure similarity between lexicals belonging to the same grammatical category.

the conversion of dependency structures in computationally effective trees (for the above kernels) is not straightforward.


Section 2 has already described the kind of features generated by SK, STK and PTK.

it is interesting to analyze what happens when SPTK is applied.


all f occurrences will always have span less than x for x ≥ ).


In Nissim's (2006) feature set, there are a couple of features that capture NP-internal information, such as determiner, NP length, and NP type.

there is only one feature that captures the syntactic context of an NP, grammatical role, which is computed based on the parse tree in which the NP resides.


Comparing each of Baseline+Ana and Baseline+Lexical+Ana with the corresponding experiments in Table 3, we see that the addition of anaphoricity features yields a mild performance improvement, which is consistent over all three classes.

comparing the last column of the two tables, we can see that in the presence of the structured features, the anaphoricity features do not contribute positively to overall performance.


Synset replacement using a similarity metric shows an improvement over using words alone.

the improvement in classification accuracy is marginal compared to sense-based representation without synset replacement (Similarity Met-ric=NA).


Again, it is only 1% over the vanilla setting that uses combination of synset and words.

the similarity metric is not sophisticated as LIN or LCH.


The assumption underlying our analysis is that a document contains description of only one topic.

reviews are generic in nature and tend to express contrasting sentiment about sub-topics .


Because Web 1T data is just n-gram statistics, rather than a collection of normal documents, it does not provide co-occurrence statistics of any random word pairs.

it provides a nice approximation to the particular co-occurrence statistics we are interested in, which are, predicate -argument pairs.


At the beginning, sentiment lexicons were designed to include only those words that express sentiment, that is, subjective words.

in recent years, sentiment lexicons started expanding to include some of those words that simply associate with sentiment, even if those words are purely objective (e.g., Velikovich et al.


In turn this implies that the reranking model must not rerank 75% of times and rerank the other 25% of times, someway contrasting the evidence provided by the baseline model score.

using our WRR strategy, we can tune the reranking model to maximize reranking effect and recover from reranking errors applying WRR.


The standard MELM with n-gram features suffers drastically as we sample more aggressively.

the binary n-gram MELM(Feat-I) does not appear to be hurt by aggressive subsampling, even when 99% of the negative examples are discarded.


In fact, the short-list approach in (Schwenk, 2007) and the adaptive importance sampling in (Bengio and Senecal, 2008) have exactly this intuition.

in the multi-class setup, subsampling like this has to be very careful.


During a perceptron update, an incorrect prediction, corresponding to the current best edge in the agenda, is penalized, and the corresponding gold edge is rewarded.

in our scenario it is not obvious what the corresponding gold edge should be, and there are many ways in which the gold edge could be defined.


The quality of the parse tree can reflect both the grammaticality of the surface string and the quality of the trained grammar model.

there is no direct way to automatically evaluate parse trees since output word choice and order can be different from the gold-standard.


The best lexical category accuracy of 77% is achieved when using a supertagger with a β level 0.075, the level for which the least lexical category disambiguation is required.

compared to the 93% lexical category accuracy of a CCG parser (Clark and Curran, 2007), which also uses a β level of 0.075 for the majority of sentences, the accuracy of our grammaticality improvement system is much lower.


(2007), which maintains a queue of hypotheses during search, and performs learning to ensure that the highest scored hypothesis in the queue is correct.

in easy-first search, hypotheses from the queue are ranked by the score of their next action, rather than the hypothesis score.


(2006), who aimed at building a dialogue system for a situated agent giving instructions in a virtual 3D world.

their approach was concerned with choosing the type of reference to use (definite or indefinite, pronominal, bare or modified head noun), and not with the content of the reference; and their data set consisted of only 1242 referring expressions.


An early machine learning ap- proach to content selection was presented by Jordan and Walker (2000;; they were also interested in an exploration of the validity of different psycholinguistic models of reference production, including Grosz and Sidner's (1986) model of discourse structure, the conceptual pacts model of Clark and colleagues, and the intentional influences model developed by .

their data set consists of only 393 referring expressions, compared to our 16,358, and these expressions had functions other than identification; most importantly, the entities referred to were not part of a shared visual scene as is the case in our data.


The alignment approach would appear to be preferable on the grounds of computational cost: we would expect that retrieving a previously-used referring expression, or parts thereof, generally requires less computation than building a new referring expression from scratch.

if the context has changed in any way, then a previously-used form of reference may no longer be effective in identifying  the intended referent, and recomputation may be required.


The smallest agreement lies at 3424 instances (68.2%) between TradREG (the least successful model) and Alignment+Ind (the most successful model).

they also each predict correct solutions that the other misses: 493 (10.0%) for TradREG and 1031 (20.8%) for Alignment+Ind.


First, we have demonstrated that a model using all these features to predict content patterns in subsequent references in shared visual scenes delivers an Accuracy of 58.8% and a DICE score of 0.81, outperforming models based only on features inspired by one of the two approaches.

we found that the features based on traditional REG considerations do not contribute as much to this score as those based on the alignment approach, and that dropping the traditional REG features does not significantly hurt the performance of a model based on alignment and theory-independent features.


Typically, POS tagging and dependency parsing are modeled in a pipelined way.

the pipelined method is prone to error propagation, especially for Chinese.


The secondorder model of Carreras (2007) incorporates both sibling and grandparent parts, and needs O(n 4 ) parsing time.

the grandparent parts are restricted to those composed of outermost grandchildren.


Based on the above illustration, we can see that joint models of version 1 are more efficient with regard to the number of POS tags for each word, but fail to incorporate syntactic surrounding features and POS trigram features in the DP structures.

joint models of version 2 can incorporate both aforementioned feature sets, but have higher complexity.


From another perspective, the joint model is capable of preferring the right tag with the help of syntactic structures, which is impossible for the baseline sequential labeling model.

pairs like {NN, NR}, {VV, VA} and {NN, JJ} only slightly influence the syntactic structure when mis-tagged.


For patterns marked by ♡, the error rate of the joint model usually increases by large margin.

the proportion of these patterns is substantially decreased, since the joint model can better resolve these ambiguities with the help of syntactic knowledge.


He conducts primitive experiments on English Penn Treebank, and shows that parsing accuracy can be improved from 91.5% to 91.9%.

he finds that the model is unbearably timeconsuming.


Each production in the optimum tree should satisfy this principle: the rule used in this production appears in the whole corpus as frequently as possible.

due to translation diversity and word alignment error, the real constituent tree of the target sentence may not be contained in the candidate projected constituents.


For example, we need to determine that the implicit determiner associated with biological products is universal, and hence, we have IMP ≫ Post.

the determiner "A" associated with general safety test is existential, and hence, we have Post ≫ A.


The most dramatic improvement is for determiners, and indeed, our features were designed for this case.

the performance gains are not very high for implicit determiners, and further investigation is needed.


Here, there is no direct method to evaluate the correctness of the translation.

indirect evaluations are possible, for example, by studying improvement in textual entailment tasks.


In practical applications where large PCFGs are empirically estimated from data sets, the standard conditions mentioned above for the polynomial time approximation of the partition function are usually met.

there are some degenerate cases for which these standard conditions do not hold, resulting in exponential time behaviour of the fixed-point iteration method.


This means that the running times in the last row of Table 1 can be reduced by treating C 3 differently from the other strongly connected components.

the running time for C 1 dominates the total time consumption.


In the work of Hall and Novák (2005) and of Attardi and Ciaramita (2007), D contains all nodes in the input parse tree.

one advantage of parse correction is its ability to focus on specific attachment types, so an additional criterion for choosing dependents is to look separately at those dependents that correspond to difficult attachment types.


Without any restriction on transition functions in T , these functions might have an infinite domain, and could thus encode even nonrecursively enumerable languages.

in standard practice for natural language parsing, transitions are always specified by some finite mean.


11,12  Lateen strategies may seem conceptually related to co-training (Blum and Mitchell, 1998).

bootstrapping methods generally begin with some labeled data and gradually label the rest (discriminatively) as they grow more confident, but do not optimize an explicit objective function; EM, on the other hand, can be fully unsupervised, relabels all examples on each iteration (generatively), and guarantees not to hurt a well-defined objective, at every step.


13 Co-training classically relies on two views of the data -redundant feature sets that allow different algorithms to label examples for each other, yielding "probably approximately correct" (PAC)-style guarantees under certain (strong) assumptions.

lateen EM uses the same data, features, model and essentially the same algorithms, changing only their objective functions: it makes no assumptions, but guarantees not to harm the primary objective.


As in experiment #3 ( §4.1), we modified the base system in exactly one way: we swapped out gold part-of-speech tags and replaced them with a flat distributional similarity clustering.

to simpler models, which suffer multi-point drops in accuracy from switching to unsupervised tags (e.g., 2.6%), our new system's performance degrades only slightly, by 0.2% (see Tables 4 and 5).


One of the top performing models of spelling correction (Bergsma et al., 2010) is based on web-scale n-gram counts, which reflect both syntax and meaning.

even with a large-scale n-gram corpus, data sparsity can hurt performance in two ways.


This may only indicate that people who misrepresent their gender are simply consistent across different aspects of their online presence.

the effort involved in maintaining this deception in two different places suggests that the blog labels on the Twitter data are largely reliable.


Previous work focused on aggregation of sentiment from all users.

in this work we show that it is beneficial to distinguish expert users from non-experts.


As with the joint all model, test tweets are ranked according to the SVM's score.

the model considers only the tweets of expert users in the test set.


Most traditional summarization methods treat their outputs as static and plain texts, which fail to capture user interests during summarization because the generated summaries are the same for different users.

users have individual preferences on a particular source document collection and obviously a universal summary for all users might not always be satisfactory.


in (2003) pre-define several topic concepts, assuming users will foresee their interested topics and then generate the topic biased summary.

such assumption is not quite reasonable because user interests may not be forecasted, or pre-defined accurately as we have explained in last section.


have proposed a summarization biased to neighboring reading context through anchor texts.

such scenario does not apply to contexts without human-edited anchor texts like Wikipedia they have used.


As presented so far, the search performed in Step 3 is admissible (or exact) -the true shortest path is found.

the search space in MT can be quite large.


§4 for definition) can take word-order into account (Smolensky, 1990) or even some more complex syntactic relations, as described in Clark and Pulman (2007).

the dimensionality of sentence vectors produced in this manner differs for sentences of different length, barring all sentences from being compared in the same vector space, and growing exponentially with sentence length hence quickly becoming computationally intractable.


(2008) or Zettlemoyer and Collins (2005) which rely on unambiguous training data where every sentence is paired only with its meaning.

chen, Kim and Mooney allow their training examples to exhibit the kind of uncertainty about sentence meanings human learners are likely to have to deal with by allowing for sentences to be associated with a set of candidate-meanings, and the correct meaning might not even be in this set.


By using several heuristics to define an effective portion of constituent trees, and training the classifiers using ACE relation sub-types (rather than on types), they achieved an impressive 75.8% F-measure.

as pointed out in (Nguyen et al., 2009), such heuristics are tuned on the target relation extraction task and might not be appropriate to compare against the automatic learning approaches.


Unlike ACE, this allows evaluators to measure performance without exhaustively annotating documents, allows for balance between rare and common relations, and implicitly measures coreference without requiring explicit annotation of answer keys for coreference.

because the evaluation only measures performance on the set of queries, many relation instances will be unscored.


When a more specific answer is present in the document, generic references have been treated as incorrect.

sometimes there is not a more specific reference; for example an article written before a drug has been released may never name the drug.


We find that precision numbers against Freebase are low, below 10%.

these numbers are not reliable mainly because many correct instances found by our models are missing in Freebase.


Supervised learning can discover more general patterns (Kambhatla, 2004;Culotta and Sorensen, 2004).

this approach requires labeled data, and most work only carry out experiments on small data set.


Researchers also use topic models to perform dimension reduction on features when they cluster relations (Hachey, 2009).

they do not explicitly model entity types.


The learning algorithm is actually fast enough to do this automatically after each labeling action.

we found such a dynamically changing interface to be frustrating for users (e.g., words they wanted to label would move or disappear).


This is essentially the common feature-selection method for identifying the most salient features in text classification (Sebastiani, 2002).

we use both L and probabilistically-labeled instances from U to compute IG(f k ), to better reflect what the model believes it has learned.


These pathological cases represent potential pitfalls that could be alleviated with additional user studies and training.

we note that the active dual interface is not particularly worse in these cases, it is simply not significantly better, as in the other 13 trials.


We present the complete approach in three parts by describing the factored representation of the lexicon (Section 5), techniques for proposing potential new lexemes and templates (Section 6), and finally a complete learning algorithm (Section 7).

the next section first reviews the required background on semantic parsing with CCG.


For this reason, UBL never proposes the lexical item, cheapest NP\(S|NP)/(S|NP) : λ f λ g.argmin(λ x. f (x) ∧ g(x), λ y.cost(y)), which is used to parse the sentence in Figure 2.

fUBL uses a lexeme learned from the same word in different contexts, along with a template learnt from similar words in a similar context, to learn to per-  figure 2: An example learned parse.


The results in Table 3 are similar to Banko and Etzioni's findings that a set of eight POS patterns cover a large fraction of binary verbal relation phrases.

their analysis was based on a set of sentences known to contain either a company acquisition or birthplace relationship, while our results are on a random sample of Web sentences.


For AL, the choice as to which labels are used (as a result of voting, bypassing or other) also has an influence on the selection.

we had to keep the sequence of the selected sentences fixed in the simulations reported above.


It can be argued that the use of short listings simplifies the problem of attribute extraction, since short listings can be easily annotated and one can apply supervised learning approach to extract product attributes.

as the size of the data grows, obtaining labeled training set on the scale of millions of listings becomes very expensive.


The web context is subsequently used to extract additional automobile brands, which result in a total of 5701 brands.

the reported results in (Nadeau el al 2006) have low precision, in some case less than 50%.


Because an influenza patient usually requires anti-influenza drugs, this approach is reasonable.

in most countries, antiinfluenza drugs are not available at the drug store (only hospitals provide such drugs).


From Table 4, we observe that NB, CRM and TAM, as generative models, tend to suggest general tags such as "novel", "literature", "classic" and "France", and fail in suggesting specific tags such as "Alexandre Dumas" and "Count of Monte Cristo".

wTM succeeds in suggesting both general and specific tags related to the book.


Finally, we find that user history can be a good indicator of rumors.

we believe that this feature could be more helpful with a complete user set and a more comprehensive history of their activities.


These feature schemas are not the only possible ones-they were empirically selected for the specific purpose of augmenting the Charniak parser.

much subsequent work has tended to use these same features, albeit sometimes with extensions for specific purposes (e.g.


In Chambers's work, discriminative classifiers -maximum entropy (MaxEnt) classifiers were used by incorporating linguistic features and temporal constraints for training, which outperforms the previous temporal language models on a subset of Gigaword Corpus (Graff et al., 2003).

the conventional methods have some limitations because they predict creation time of documents mainly based on feature-based models without understanding content of documents, which may lead to wrong predictions in some cases.


In this case, it is difficult to recognize the relative temporal relations.

timeliness can be leveraged to determine the relations as a heuristic method.


For example, triple r d,ex =< d, ex, −1 > means that the time of extraction ex is one year before the time of document d. Extractions from different documents have no connections.

there are a great number of extractions referring to the same event.


When only 1,000 documents are initially labeled with timestamps, the confidence boosting model can propagate their timestamps to more than 400,000 documents with an accuracy of 0.494, approximately 12.8% relative improvement over the BFS counterpart, which proves effectiveness of the confidence boosting model.

as shown in table 5, hardly can the propagation process propagate timestamps to all documents.


In addition, they used KL-divergence instead of normalized log likelihood ratio to measure differences between a document and a time period's language model.

these methods are based on temporal language models so they also suffer from the problem of the method of de Jong et al.


Given only syntactic features, we may be drawn to conclude that they share similar temporal relationships.

in the first line of text, the events temporally OVERLAP, while in the second line they do not.


If the relations are randomly distributed, we should expect their distribution to follow that of the temporal classes as shown in Table 1.

we see that many of the relations do not follow this distribution.


This part of the model is parametric, operating over a fixed number of tags T , and is identical to the formulation of tag transitions in the Bayesian HMM (Goldwater and Griffiths, 2007).

we replace the BHMM's emission distribution with the morphologically-informed distributions described below.


We find no improvement on tagging performance in English when adding morphology, compared to the MORTAGNOSEG baseline in which words are not segmented.

we do see a small but significant improvement over the BHMM for both of these models, due to the replacement of the multinomial emission distribution in the BHMM with the PYP.


Conversely, in Spanish we saw less difference on the morphology task between models with categories inferred solely from morphemic patterns and models that also used local syntactic context for categorisation.

in Spanish we saw an improvement in the tagging task when morphology information was included.


(2012) present a model that is similar to ours, using a noisy channel model implemented with a finite-state transducer to learn about phonetic variability while clustering distinct tokens into lexical items.

(like the earlier lexical-phonetic learning model of Feldman et al.


The unigram model is a reasonable segmenter, though not quite as good as the bigram model, with boundary F of 76.4 and token F of 60.9 (compared to 79.8 and 64.4 using the bigram model).

it is not good at normalizing variation; its mtk score is comparable to the baseline at 44.8% 5 .


Because there is little available training data, and because social media language changes rapidly (Eisenstein, 2013b), fully supervised training is generally not considered appropriate for this task.

due to the extremely high-dimensional output space -arbitrary sequences of words across the vocabulary -it is a very challenging problem for unsupervised learning.


We are left with a difference in expected feature counts, as is typical in log-linear models.

unlike the supervised case, here both terms are expectations: the outer expectation is over all target sequences (given the observed source sequence), and the nested expectation is over all source sequences, given the target sequence.


It may appear that all we have gained by applying sequential Monte Carlo is to convert a computational problem into a statistical one: a naive sampling approach will have little hope of finding the small high-probability region of the highdimensional label space.

sequential importance sampling allows us to address this issue through the proposal distribution, from which we sample the candidate words t n .


Microblogs such as Twitter, Sina Weibo (a popular Chinese microblog service) and Facebook have received increasing attention in diverse research communities (Han and Baldwin, 2011;Hawn, 2009, inter alia).

to traditional text domains that use carefully controlled, standardized language, microblog content is often informal, with less adherence to conventions regarding punctuation, spelling, and style, and with a higher proportion of dialect or pronouciation-derived orthography.


If we look at the normalization DanielVeuleman to Daniel Veuleman, we can see that it is only applicable when the exact word DanielVeuleman occurs.

we wish to learn that it is uncommon for the letters l and v to occur in the same word sequentially, so that be can add missing spaces in words that contain the lv character sequence, such as normalizing phenomenalvoter to phenomenal voter.


However, we wish to learn that it is uncommon for the letters l and v to occur in the same word sequentially, so that be can add missing spaces in words that contain the lv character sequence, such as normalizing phenomenalvoter to phenomenal voter.

there are also cases where this is not true, for instance, in the word velvet, we do not wish to separate the letters l and v. Thus, we shall describe the process we use to decide when to apply these transformations.


This includes orthographic errors, abbreviations and slang.

this is generally not enough to detect lexical variants, as many words share similar contexts, such as already, recently and normally.


This can be captured by allowing each speaker to have a different θ and ζ in each stage of the campaign.

we expect that a speaker draws from his ideological lexicon similarly across different epochs-there is a single ψ shared between different epochs.


In MIX, there are no temporal effects between cue terms, although the structure of our ideology tree encourages the speaker to draw from coarse-grained ideologies over fine-grained ideologies.

the strong Markovian dependency between states in NORES would encourage the model to stay local within the ideology tree.


The parameters of the token based ITGs can be estimated using expectation maximization through an efficient dynamic programming algorithm in conjunction with beam pruning (Saers and Wu, 2011).

to token based ITGs, each rule in a segmental ITG grammar can contain more than one token in both input and output languages.


It is not immediately obvious how one might quantify such a notion of "idea-based" influence.

the mechanism used in the scientific community for giving credit to prior work is citation.


We will show that such an assumption is quite reasonable under our model, since the prototype projections successfully tease out the proper semantics from these aggregate representations.

it is natural to wonder whether one can do better if one incorporates the compositional model into the training of the word representations in the first place.


This result shows that our learning model successfully captures good representation within cocompositionality of additive model.

to other previous compositional models, our model does not require estimating a large number of parameters for computation of compositional vectors and word representation itself is more suitable for it.


The appeal of this kind of simple approach is its intuitive geometric interpretation and its robustness to various datasets.

it may not be sufficiently expressive to represent the various factors involved in compositional semantics, such as syntax and context.


Many of these models need to optimize n × n parameters, which may be large.

our model only needs two hyper-parameters: the number of prototype words m and dimensional reduction k in SVD (Table 6).


For example, in the phrase creative new idea, the idea is both new and creative, so we would expect a similar impact of modification by both adjectives.

we predict that in rigid order cases, one adjective, the one closer to the noun, will dominate the meaning of the phrase, distorting the meaning of the noun by a significant amount.


To understand the message-passing algorithm in this study, it is helpful to think of our model as a simplified Hidden Semi-Markov Model (HSMM), in which the letters represent the states and the speech features are the observations.

unlike in a regular HSMM, where the state sequence is hidden, in our case, the state sequence is fixed to be the given letter sequence.


By comparing our model to the grapheme baseline, we can see the advantage of modeling the pronunciations of a letter using a mixture model, especially for a language like English which has many pronunciation irregularities.

even for languages with straightforward pronunciation rules, the concept of modeling letter pronunciations using mixture models still applies.


Previous work on machine comprehension (e.g., semantic modeling) has made great strides, but primarily focuses either on limited-domain datasets, or on solving a more restricted goal (e.g., open-domain relation extraction).

mCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension.


Yet these techniques are necessarily evaluated individually, rather than by how much they advance us towards the end goal.

the goal of semantic parsing is the machine comprehension of text (MCT), yet its evaluation requires adherence to a specific knowledge representation, and it is currently unclear what the best representation is, for open-domain text.


The intent was to produce a set of stories and questions that varied in difficulty so that research work can progress grade-by-grade if needed.

we found little difference between grades in the corpus.. After gathering the stories, we manually curated the MC160 corpus by reading each story set and correcting errors.


ShefLM achieves a high compression ratio when it stores counts of ngrams in the training corpus.

when this method stores probabilities of ngrams, the advantage of using compression is limited because floating-point numbers are difficult to compress.


In prior work, the endmarker symbol has been used to indicate whether an ngram is in the trie.

there is no need to distinguish whether the node of the tree is included in the language model because all nodes of a backwards suffix tree which represents ngrams surely exist in the model.


In this paper, we focus on penalized maximum likelihood estimation in log-linear models.

to language models based on unstructured norms such as 2 (quadratic penalties) or 1 (absolute discounting), we use tree-structured norms (Zhao et al., 2009;Jenatton et al., 2011).


The generalization properties of this algorithm is as good as the generalization obtained with the T 2 penalty, if not better.

it has the constant value non-branching path property, which  Table 1: Properties of the algorithms proposed in this paper.


Phrase-based models have shown a very strong performance when translating between languages that have similar word orders.

they are not able to adequately capture the complex relationships that exist between the word orders of languages of different families such as English and Chinese.


A source span boundary feature in Figure 2(b) that is defined on the source parse tree is also a local feature.

a target span boundary feature in Figure 2(c), which assesses the target parse structure, is a non-local feature.


Non-local features enable us to model the target parse structure in a derivation.

it is computationally expensive to calculate the expected values of non-local features over D (s)  words and result in an extremely large number of states during dynamic programming.


The metrics provide an effective overall ranking, as the systems with high scores generally make fewer errors.

the metrics do not convey the significant variation in the types of errors systems make.


Some of these cases are correct, such as variation in person between mentions inside and outside of quotes.


4 By fixing each of the other error types in isolation, we can get a sense of the gain if just that error type is addressed.

it also means some mentions are incorrectly placed in the same cluster, causing some negative scores.


No single source of errors stands out as the most substantial challenge today.

it is worth noting that while confidence measures can be used to reduce precision-related errors, no system has been able to effectively address the recall-related errors, such as Missed Entities.


Unfortunately, it is difficult to compare directly to the results of both systems, since they reported results on portions of ACE and CoNLL datasets using gold mentions.

our approach provides independent evidence for the benefit of NEL, and joint modeling in particular, since it outperforms the state-of-the-art Stanford sieve system (winner of the CoNLL 2011 shared task (Pradhan et al., 2011)) and other recent comparable approaches on benchmark datasets.


One limitation of our approach is that it only learns the properties that are present in CSN antecedents.

aSN antecedents have additional properties which are not always captured by CSN antecedents.


We propose a scalable semi-supervised feature engineering approach.

to previous works using pre-defined taskspecific features with fixed values, we dynamically extract representations of label distributions from both an in-domain corpus and an out-of-domain corpus.


Therefore, MI can show the tendency of two strings forming one word.

previous works mainly focused on the balanced case, i.e., the MI of strings with the same length.


Note that the SSFs are still calculated using all the unlabeled data.

each iteration in the algorithm uses unlabeled data with different sizes.


Our interpretation is that the correct nominative reading is pruned from the 0-order lattice.

the higher-order models can put less weight on 0-order features as they have access to more context to disambiguate the sequence.


The sentences in the corpus have a relatively simple syntax, though many also exhibit long distance dependencies.

these conversations are pragmatically complex.


For the first baseline 34 examples are like this; for the second and third baselines it's 32.

this problem occurs only once with the fourth baseline, and all the trades predicted by our method are complete, making random choice unnecessary.


Previous work has shown that for many common NLP tasks, 7 Turkers' average score can match expert annotations (Snow et al., 2008).

we use 15 Turkers because we had no gold-standard data and because we were not sure how difficult the task is.


The best performing method for the Action genre is CP+Web Search at 87.67%, while the best performing method for the Romance genre is PMI+Web search at 88.52%.

pMI+Web Search does not beat Cp+Web Search on average over both genres we tested, even though the Mechanical Turk HIT for Cp specifies that the order of the events matters: a more stringent criterion.


In particular, coreference patterns tend to be very similar in translations of a text, and this fact has been exploited with good results to project coreference annotations from one language into another by using word alignments (Postolache et al., 2006;Rahman and Ng, 2012).

what is true in general need not be true for all types of linguistic elements.


Our empirical results have shown that this approach outperforms a previous graph-based approach with an absolute gain of 9%.

while these graph-based approaches perform effectively when the agent has perfect knowledge or perception of the environment (e.g., 84%), they perform rather poorly when the agent has imperfect perception of the environment (e.g., 45%).